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A successful first Global Dialogue on AI Governance should move beyond general principles and produce actionable, globally relevant outcomes. First, it should establish a shared baseline framework for AI risk classification, enabling governments and private sector actors to distinguish between low-risk and high-risk AI systems. Without this, regulatory fragmentation will continue to grow. Second, the Dialogue should result in practical guidance for implementation, especially for developing regions. Many countries lack the institutional and technical capacity to operationalize AI governance frameworks, which creates uneven adoption and increased exposure to misuse. Third, it should promote inclusive participation in AI governance, ensuring that perspectives from Africa and other underrepresented regions are meaningfully integrated into global standards, rather than treated as secondary considerations. Fourth, the Dialogue should encourage public-private collaboration mechanisms, particularly in areas such as cybersecurity, data protection, and AI system auditing. Governments alone cannot effectively regulate AI without technical cooperation from industry. Finally, success should be measured by the creation of clear follow-up structures, including working groups or regional implementation tracks, to ensure continuity beyond the initial Dialogue. Without concrete outputs and accountability mechanisms, the Dialogue risks becoming purely consultative rather than impactful.

• Safe, secure and trustworthy AI
• AI capacity-building
• Transparency, accountability, and human oversight
• Protection and promotion of human rights

These priorities reflect both immediate risks and structural gaps in current AI governance. Safe, secure and trustworthy AI is critical due to the increasing use of AI in sensitive domains, including finance, identity systems, and public services. Weak security practices expose systems to manipulation, fraud, and large-scale misuse. AI capacity-building is essential, particularly in developing regions, where there is limited technical expertise, regulatory infrastructure, and institutional readiness. Without targeted investment in skills and systems, global AI governance will remain uneven and ineffective. Transparency, accountability, and human oversight are necessary to address the growing use of opaque AI models. Organizations deploying AI must be able to explain decisions, ensure auditability, and maintain human control in high-risk scenarios. Protection and promotion of human rights is a foundational concern, especially as AI systems increasingly influence access to services, employment, and information. Bias, discrimination, and data misuse must be actively mitigated through enforceable safeguards. Together, these priorities balance technical risk management with ethical and societal considerations, while also addressing global inequalities in AI adoption and governance.

One critical cross-cutting issue is the intersection of AI and cybersecurity, which is not sufficiently emphasized in current governance discussions. AI is increasingly being used both as a defensive tool and as a mechanism for sophisticated cyberattacks, including automated phishing, deepfake-based fraud, and system exploitation. Governance frameworks must explicitly address this dual-use risk. Another emerging issue is data sovereignty and unequal data access. Many developing countries contribute data that is used to train global AI systems but do not benefit proportionally from the resulting technologies. This creates long-term economic and strategic imbalances. Additionally, the concentration of AI development within a small number of global companies raises concerns about market dominance, dependency, and reduced innovation in less-developed ecosystems. This has implications for both governance and digital sovereignty. Finally, there is a growing need to address informal and unregulated AI deployment, particularly in regions where enforcement mechanisms are weak. Many AI systems are already being used without oversight, increasing the risk of harm. Addressing these cross-cutting issues is essential to ensure that AI governance is both realistic and globally inclusive, rather than narrowly focused on well-regulated environments.

Governance gaps in AI are already having practical consequences across Africa's technology and business landscape, particularly in cybersecurity, digital services, and data management. One of the most significant challenges is the lack of enforceable standards for safe and secure AI deployment. Many organizations are adopting AI tools without proper risk assessment, making systems vulnerable to misuse, fraud, and data breaches. This is especially critical in sectors such as finance and identity systems, where weak oversight can have large-scale impacts. A second major gap is limited AI capacity and technical expertise. Many institutions lack the skills and infrastructure required to implement, monitor, or regulate AI systems effectively. This creates dependency on external technologies, often without full understanding of associated risks. There are also concerns around transparency and accountability. AI systems are increasingly used in decision-making processes, yet there is little visibility into how these decisions are made. This raises risks of bias, unfair outcomes, and lack of recourse for affected individuals. At the same time, there are clear opportunities. AI has strong potential to accelerate digital transformation, improve service delivery, and enhance cybersecurity capabilities when properly governed. For example, AI-driven threat detection can significantly strengthen defenses against evolving cyber risks. Additionally, improving governance frameworks presents an opportunity for African countries to build trust, attract investment, and participate more actively in the global AI ecosystem. Addressing these gaps is essential to ensure that AI adoption is both secure and inclusive, rather than increasing existing vulnerabilities and inequalities.

The AI Dialogue can play a critical role in bridging fragmentation and aligning global efforts on AI governance. First, it can serve as a platform to develop shared principles and practical coordination mechanisms between governments, private sector actors, and technical communities. Currently, AI governance approaches are evolving in isolation, leading to inconsistencies that create regulatory uncertainty and operational challenges across borders. Second, the Dialogue can facilitate knowledge transfer and capacity-sharing, particularly between developed and developing regions. Many countries lack the institutional and technical capabilities to implement AI governance effectively, and structured cooperation is needed to close this gap. Third, it can promote trust-building among stakeholders by encouraging transparency in how AI systems are developed, deployed, and regulated. This is essential for addressing concerns around misuse, security risks, and human rights impacts. Additionally, the Dialogue can support the development of interoperable governance approaches, helping ensure that different national and regional frameworks can work together rather than conflict. This is particularly important for global industries and digital services that operate across multiple jurisdictions. Finally, the AI Dialogue should act as a catalyst for ongoing collaboration, not just a one-time event. Establishing follow-up mechanisms, working groups, and regional engagement platforms will be essential to maintain momentum and translate discussions into implementation. Without sustained international cooperation, AI governance risks becoming fragmented, uneven, and ineffective in addressing global challenges.

The AI Dialogue should build on existing global and regional initiatives while addressing the fragmentation between them. Key frameworks include the OECD AI Principles, which provide widely recognized guidance on trustworthy AI, and the UNESCO Recommendation on the Ethics of Artificial Intelligence, which emphasizes human rights and ethical standards. In addition, the European Union AI Act represents one of the most advanced regulatory approaches, offering practical insights into risk-based governance. Multi-stakeholder initiatives such as the Global Partnership on Artificial Intelligence and the African Union digital transformation strategies also provide valuable platforms for collaboration and regional alignment. However, these efforts often operate in parallel, with limited coordination and varying levels of participation from developing regions. The added value of the AI Dialogue lies in its ability to act as a neutral, inclusive coordination platform that connects these initiatives and reduces duplication. It can help translate high-level principles into practical, globally adaptable guidance, particularly for countries with limited regulatory capacity. Furthermore, the Dialogue can amplify underrepresented perspectives, especially from Africa and other developing regions, ensuring that global AI governance is not shaped solely by a small group of technologically advanced economies. By linking existing frameworks and fostering structured collaboration, the AI Dialogue can move the global community from principles to implementation, while promoting greater coherence and inclusivity in AI governance.

Different stakeholders can contribute to the AI Dialogue by focusing on their comparative strengths, while aligning around shared objectives. Governments should provide policy direction, regulatory frameworks, and enforcement mechanisms, ensuring that AI systems operate within clear legal and ethical boundaries. The private sector should contribute technical expertise, real-world implementation insights, and risk assessments, particularly on system security, scalability, and operational challenges. Academia can support with independent research, evaluation methodologies, and evidence-based analysis, while civil society plays a critical role in advocating for human rights, inclusion, and accountability. Technical communities should contribute to standards development, auditing tools, and open technical benchmarks. To be effective, the AI Dialogue should adopt a structured and outcome-oriented format. First, discussions should be organized into focused thematic working groups, each tasked with producing specific outputs such as policy recommendations, implementation guidelines, or risk frameworks. Second, the Dialogue should include regional tracks or breakout sessions, allowing stakeholders from different regions to address context-specific challenges while feeding into global outcomes. Third, there should be a strong emphasis on practical case studies and use cases, rather than purely theoretical discussions, to ensure relevance and applicability. Fourth, the process should incorporate multi-stage engagement, including pre-dialogue submissions, interactive sessions during the event, and post-dialogue follow-up mechanisms. Finally, clear deliverables and accountability structures should be defined, ensuring that discussions translate into measurable progress rather than remaining purely consultative.

Global discussions on AI governance continue to reflect a limited set of dominant perspectives, with several important voices underrepresented. First, stakeholders from developing regions, particularly Africa, remain insufficiently included in shaping global AI policies. Despite being directly affected by imported technologies, these regions often have limited influence over how standards are defined. Second, small and medium-sized enterprises (SMEs) and local technology providers are underrepresented. Most discussions are dominated by large multinational companies, yet SMEs are key drivers of innovation and are often the ones implementing AI in local contexts with fewer resources. Third, there is a gap in representation from technical practitioners working on the ground, including cybersecurity professionals, system implementers, and engineers. Their practical experience with deployment risks, system vulnerabilities, and operational constraints is often missing from high-level policy debates. Additionally, civil society groups from non-Western contexts are not sufficiently engaged, particularly those working on digital rights, local languages, and community-level impacts of AI systems. To address these gaps, the AI Dialogue should adopt inclusive participation mechanisms. This includes targeted outreach, financial and logistical support for participants from underrepresented regions, and regional consultation processes ahead of the main Dialogue. Furthermore, contributions should be accepted in multiple formats and languages, reducing barriers to participation. Dedicated sessions or quotas for underrepresented groups can also ensure meaningful inclusion rather than symbolic representation. Ensuring diverse participation is essential for developing AI governance frameworks that are globally relevant, practical, and equitable.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel discussions and adopt interactive, problem-solving formats. One effective approach is scenario-based simulations, where participants are presented with real-world AI risk cases (e.g., AI-enabled fraud, biased decision systems, or critical infrastructure attacks) and collaborate across sectors to develop response strategies. This encourages practical thinking and highlights gaps in current governance approaches. Second, multi-stakeholder "policy labs" can be used to co-develop concrete outputs such as model regulations, audit frameworks, or risk classification systems. These small, focused groups should include representatives from government, industry, and technical communities to ensure balanced perspectives. Third, the Dialogue should incorporate live case study reviews, where organizations present actual AI deployments, including challenges and failures. This promotes transparency and allows participants to learn from real implementation experiences rather than theoretical discussions. Another valuable format is interactive roundtables with rotating participation, enabling broader engagement and preventing discussions from being dominated by a small group of voices. Additionally, the use of digital collaboration platforms can allow remote participants to contribute in real time, increasing inclusivity and global participation beyond those physically present. Finally, structured "challenge sessions" could invite participants to identify unresolved governance issues and propose actionable solutions, with outcomes documented and tracked post-event. These formats would shift the Dialogue from passive discussion to active collaboration and tangible outcomes, increasing both relevance and long-term impact.

Several existing policies and practices provide practical foundations for effective AI governance, particularly when they move beyond principles into implementation. The European Union AI Act is a leading example of a risk-based regulatory approach, classifying AI systems by their potential impact and applying proportionate obligations. This model is valuable because it balances innovation with safeguards and provides clear compliance pathways for organizations. The OECD AI Principles and the UNESCO Recommendation on the Ethics of Artificial Intelligence offer globally recognized frameworks that emphasize transparency, accountability, and human rights. Their strength lies in establishing common standards that can guide national policies. From an operational perspective, AI auditing and impact assessment practices are emerging as effective tools. Organizations are increasingly adopting internal AI risk assessments, bias testing, and documentation processes before deployment. These practices improve accountability and reduce unintended harm. In addition, public-private partnerships are proving essential, particularly in areas such as cybersecurity and data governance. Collaboration between governments and industry allows for faster identification of risks and more adaptive policy responses. There are also growing efforts around open technical standards and benchmarks, which support interoperability and enable consistent evaluation of AI systems across different environments. However, while these approaches are promising, their effectiveness depends on local adaptation, enforcement capacity, and continuous review. The key lesson is that successful AI governance requires a combination of clear regulation, practical tools, and multi-stakeholder collaboration, rather than relying on high-level principles alone.